guest_blog — Published On September 5, 2020 and Last Modified On September 5th, 2020
Advanced Machine Learning Project Python Regression Structured Data Supervised

Web Applications with Flask

import pandas as pd
from sklearn.linear_model import LinearRegression
import pickle

df = pd.read_csv("FuelConsumption.csv")
#use required features

#Training Data and Predictor Variable
# Use all data for training (tarin-test-split not used)
x = cdf.iloc[:, :3]
y = cdf.iloc[:, -1]
regressor = LinearRegression()

#Fitting model with trainig data, y)

# Saving model to current directory
# Pickle serializes objects so they can be saved to a file, and loaded in a program again later on.
pickle.dump(regressor, open('model.pkl','wb'))

#Loading model to compare the results
model = pickle.load(open('model.pkl','rb'))
print(model.predict([[2.6, 8, 10.1]]))


#import libraries
import numpy as np
from flask import Flask, render_template,request
import pickle#Initialize the flask App
app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))
#default page of our web-app
def home():
    return render_template('index.html')
#To use the predict button in our web-app
def predict():
    #For rendering results on HTML GUI
    int_features = [float(x) for x in request.form.values()]
    final_features = [np.array(int_features)]
    prediction = model.predict(final_features)
    output = round(prediction[0], 2) 
    return render_template('index.html', prediction_text='CO2    Emission of the vehicle is :{}'.format(output))

Web Applications with Flask

if __name__ == "__main__":

Web Applications with Flask - Projct Structure

Project Structure

Web Applications with Flask

Now, let’s enter the required values and click on the “Predict” button and see what happens.

Web Applications with Flask

web: gunicorn app:app
pip freeze > requirements.txt

Image for post

To deploy your app on Heroku from here on, follow the steps 2–3 in my following article: Deploying a Static Web Application to Heroku

About the Author


Nakukl Lakhotia

Nakul has completed his B.Tech in Computer Science from Vellore Institute of Technology, Vellore. He is a machine learning and NLP enthusiast and has worked as a Data Science Intern in CustomersFirst Now.

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